Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations32327
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory80.0 B

Variable types

Text3
Numeric7

Alerts

product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_weight_g is highly overall correlated with product_height_cm and 2 other fieldsHigh correlation
product_width_cm is highly overall correlated with product_length_cm and 1 other fieldsHigh correlation
product_id has unique values Unique

Reproduction

Analysis started2025-09-13 07:15:48.367479
Analysis finished2025-09-13 07:15:58.178474
Duration9.81 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

product_id
Text

Unique 

Distinct32327
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size252.7 KiB
2025-09-13T10:45:58.445100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters1034464
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32327 ?
Unique (%)100.0%

Sample

1st row1e9e8ef04dbcff4541ed26657ea517e5
2nd row3aa071139cb16b67ca9e5dea641aaa2f
3rd row96bd76ec8810374ed1b65e291975717f
4th rowcef67bcfe19066a932b7673e239eb23d
5th row9dc1a7de274444849c219cff195d0b71
ValueCountFrequency (%)
1e9e8ef04dbcff4541ed26657ea517e5 1
 
< 0.1%
3bb7f144022e6732727d8d838a7b13b3 1
 
< 0.1%
cef67bcfe19066a932b7673e239eb23d 1
 
< 0.1%
9dc1a7de274444849c219cff195d0b71 1
 
< 0.1%
41d3672d4792049fa1779bb35283ed13 1
 
< 0.1%
732bd381ad09e530fe0a5f457d81becb 1
 
< 0.1%
2548af3e6e77a690cf3eb6368e9ab61e 1
 
< 0.1%
37cc742be07708b53a98702e77a21a02 1
 
< 0.1%
8c92109888e8cdf9d66dc7e463025574 1
 
< 0.1%
14aa47b7fe5c25522b47b4b29c98dcb9 1
 
< 0.1%
Other values (32317) 32317
> 99.9%
2025-09-13T10:45:59.005306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 65213
 
6.3%
3 65108
 
6.3%
c 64976
 
6.3%
e 64972
 
6.3%
9 64871
 
6.3%
1 64790
 
6.3%
d 64773
 
6.3%
5 64715
 
6.3%
7 64656
 
6.3%
f 64601
 
6.2%
Other values (6) 385789
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 646893
62.5%
Lowercase Letter 387571
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 65213
10.1%
3 65108
10.1%
9 64871
10.0%
1 64790
10.0%
5 64715
10.0%
7 64656
10.0%
4 64580
10.0%
2 64561
10.0%
0 64322
9.9%
6 64077
9.9%
Lowercase Letter
ValueCountFrequency (%)
c 64976
16.8%
e 64972
16.8%
d 64773
16.7%
f 64601
16.7%
b 64368
16.6%
a 63881
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 646893
62.5%
Latin 387571
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
8 65213
10.1%
3 65108
10.1%
9 64871
10.0%
1 64790
10.0%
5 64715
10.0%
7 64656
10.0%
4 64580
10.0%
2 64561
10.0%
0 64322
9.9%
6 64077
9.9%
Latin
ValueCountFrequency (%)
c 64976
16.8%
e 64972
16.8%
d 64773
16.7%
f 64601
16.7%
b 64368
16.6%
a 63881
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1034464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 65213
 
6.3%
3 65108
 
6.3%
c 64976
 
6.3%
e 64972
 
6.3%
9 64871
 
6.3%
1 64790
 
6.3%
d 64773
 
6.3%
5 64715
 
6.3%
7 64656
 
6.3%
f 64601
 
6.2%
Other values (6) 385789
37.3%
Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size252.7 KiB
2025-09-13T10:45:59.333966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length46
Median length30
Mean length14.95032
Min length3

Characters and Unicode

Total characters483299
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowperfumaria
2nd rowartes
3rd rowesporte_lazer
4th rowbebes
5th rowutilidades_domesticas
ValueCountFrequency (%)
cama_mesa_banho 3029
 
9.4%
esporte_lazer 2867
 
8.9%
moveis_decoracao 2657
 
8.2%
beleza_saude 2444
 
7.6%
utilidades_domesticas 2335
 
7.2%
automotivo 1900
 
5.9%
informatica_acessorios 1639
 
5.1%
brinquedos 1411
 
4.4%
relogios_presentes 1329
 
4.1%
telefonia 1134
 
3.5%
Other values (61) 11582
35.8%
2025-09-13T10:45:59.880823image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 57706
11.9%
e 57311
11.9%
o 49446
10.2%
s 48292
10.0%
i 31859
 
6.6%
_ 30817
 
6.4%
r 29659
 
6.1%
t 24506
 
5.1%
c 23049
 
4.8%
m 21093
 
4.4%
Other values (18) 109561
22.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 452387
93.6%
Connector Punctuation 30817
 
6.4%
Decimal Number 95
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 57706
12.8%
e 57311
12.7%
o 49446
10.9%
s 48292
10.7%
i 31859
 
7.0%
r 29659
 
6.6%
t 24506
 
5.4%
c 23049
 
5.1%
m 21093
 
4.7%
l 16354
 
3.6%
Other values (16) 93112
20.6%
Connector Punctuation
ValueCountFrequency (%)
_ 30817
100.0%
Decimal Number
ValueCountFrequency (%)
2 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 452387
93.6%
Common 30912
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 57706
12.8%
e 57311
12.7%
o 49446
10.9%
s 48292
10.7%
i 31859
 
7.0%
r 29659
 
6.6%
t 24506
 
5.4%
c 23049
 
5.1%
m 21093
 
4.7%
l 16354
 
3.6%
Other values (16) 93112
20.6%
Common
ValueCountFrequency (%)
_ 30817
99.7%
2 95
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483299
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 57706
11.9%
e 57311
11.9%
o 49446
10.2%
s 48292
10.0%
i 31859
 
6.6%
_ 30817
 
6.4%
r 29659
 
6.1%
t 24506
 
5.1%
c 23049
 
4.8%
m 21093
 
4.4%
Other values (18) 109561
22.7%

product_name_lenght
Real number (ℝ)

Distinct66
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.473722
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:00.084268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median51
Q357
95-th percentile60
Maximum76
Range71
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.246346
Coefficient of variation (CV)0.2113794
Kurtosis0.19172207
Mean48.473722
Median Absolute Deviation (MAD)7
Skewness-0.90282532
Sum1567010
Variance104.98761
MonotonicityNot monotonic
2025-09-13T10:46:00.303564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 2178
 
6.7%
59 2023
 
6.3%
58 1885
 
5.8%
57 1718
 
5.3%
55 1683
 
5.2%
56 1675
 
5.2%
54 1438
 
4.4%
53 1330
 
4.1%
52 1257
 
3.9%
50 1039
 
3.2%
Other values (56) 16101
49.8%
ValueCountFrequency (%)
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 8
< 0.1%
10 5
 
< 0.1%
11 7
< 0.1%
12 13
< 0.1%
13 16
< 0.1%
14 11
< 0.1%
ValueCountFrequency (%)
76 1
 
< 0.1%
72 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 1
 
< 0.1%
64 59
 
0.2%
63 515
1.6%
62 65
 
0.2%
61 65
 
0.2%
Distinct2960
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean771.51728
Minimum4
Maximum3992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:00.574477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile150
Q1339
median595
Q3972
95-th percentile2063
Maximum3992
Range3988
Interquartile range (IQR)633

Descriptive statistics

Standard deviation635.18967
Coefficient of variation (CV)0.82329935
Kurtosis4.8282118
Mean771.51728
Median Absolute Deviation (MAD)293
Skewness1.9621251
Sum24940839
Variance403465.92
MonotonicityNot monotonic
2025-09-13T10:46:00.824455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
404 94
 
0.3%
729 86
 
0.3%
703 66
 
0.2%
651 66
 
0.2%
236 65
 
0.2%
184 65
 
0.2%
303 63
 
0.2%
352 62
 
0.2%
375 60
 
0.2%
246 58
 
0.2%
Other values (2950) 31642
97.9%
ValueCountFrequency (%)
4 5
< 0.1%
8 1
 
< 0.1%
15 1
 
< 0.1%
20 1
 
< 0.1%
23 1
 
< 0.1%
26 2
 
< 0.1%
27 1
 
< 0.1%
28 1
 
< 0.1%
30 2
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
3992 1
< 0.1%
3988 1
< 0.1%
3985 1
< 0.1%
3976 1
< 0.1%
3963 1
< 0.1%
3956 1
< 0.1%
3954 2
< 0.1%
3950 1
< 0.1%
3949 1
< 0.1%
3948 1
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct19
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1887896
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:01.036002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7367668
Coefficient of variation (CV)0.79348277
Kurtosis7.267863
Mean2.1887896
Median Absolute Deviation (MAD)0
Skewness2.1941843
Sum70757
Variance3.0163589
MonotonicityNot monotonic
2025-09-13T10:46:01.207868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1 16483
51.0%
2 6262
 
19.4%
3 3858
 
11.9%
4 2425
 
7.5%
5 1483
 
4.6%
6 967
 
3.0%
7 343
 
1.1%
8 192
 
0.6%
9 105
 
0.3%
10 95
 
0.3%
Other values (9) 114
 
0.4%
ValueCountFrequency (%)
1 16483
51.0%
2 6262
 
19.4%
3 3858
 
11.9%
4 2425
 
7.5%
5 1483
 
4.6%
6 967
 
3.0%
7 343
 
1.1%
8 192
 
0.6%
9 105
 
0.3%
10 95
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 2
 
< 0.1%
17 7
 
< 0.1%
15 8
 
< 0.1%
14 5
 
< 0.1%
13 9
 
< 0.1%
12 35
 
0.1%
11 46
0.1%
10 95
0.3%

product_weight_g
Real number (ℝ)

High correlation 

Distinct2201
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2276.9608
Minimum0
Maximum40425
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:01.692781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1300
median700
Q31900
95-th percentile10850
Maximum40425
Range40425
Interquartile range (IQR)1600

Descriptive statistics

Standard deviation4279.7341
Coefficient of variation (CV)1.8795818
Kurtosis15.181689
Mean2276.9608
Median Absolute Deviation (MAD)500
Skewness3.6076942
Sum73607312
Variance18316124
MonotonicityNot monotonic
2025-09-13T10:46:01.989949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 1985
 
6.1%
300 1528
 
4.7%
150 1243
 
3.8%
400 1175
 
3.6%
100 1173
 
3.6%
500 1088
 
3.4%
250 986
 
3.1%
600 939
 
2.9%
350 824
 
2.5%
700 732
 
2.3%
Other values (2191) 20654
63.9%
ValueCountFrequency (%)
0 4
 
< 0.1%
2 5
 
< 0.1%
25 1
 
< 0.1%
50 310
1.0%
53 1
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 6
 
< 0.1%
61 1
 
< 0.1%
ValueCountFrequency (%)
40425 1
 
< 0.1%
30000 142
0.4%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 2
 
< 0.1%
29600 5
 
< 0.1%
29500 2
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.856498
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:02.271967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile65
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.95846
Coefficient of variation (CV)0.5495912
Kurtosis3.5025323
Mean30.856498
Median Absolute Deviation (MAD)8
Skewness1.749933
Sum997498
Variance287.58935
MonotonicityNot monotonic
2025-09-13T10:46:02.575816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 5357
 
16.6%
20 2771
 
8.6%
30 1987
 
6.1%
18 1484
 
4.6%
25 1360
 
4.2%
17 1291
 
4.0%
19 1260
 
3.9%
40 1183
 
3.7%
22 954
 
3.0%
35 941
 
2.9%
Other values (89) 13739
42.5%
ValueCountFrequency (%)
7 1
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 3
 
< 0.1%
11 16
 
< 0.1%
12 15
 
< 0.1%
13 20
 
0.1%
14 40
 
0.1%
15 47
 
0.1%
16 5357
16.6%
ValueCountFrequency (%)
105 148
0.5%
104 19
 
0.1%
103 9
 
< 0.1%
102 19
 
0.1%
101 18
 
0.1%
100 119
0.4%
99 16
 
< 0.1%
98 16
 
< 0.1%
97 7
 
< 0.1%
96 4
 
< 0.1%

product_height_cm
Real number (ℝ)

High correlation 

Distinct102
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.95595
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:02.888295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320.5
95-th percentile44
Maximum105
Range103
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation13.637246
Coefficient of variation (CV)0.80427497
Kurtosis6.7246472
Mean16.95595
Median Absolute Deviation (MAD)6
Skewness2.1496596
Sum548135
Variance185.97449
MonotonicityNot monotonic
2025-09-13T10:46:03.176209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2507
 
7.8%
15 1992
 
6.2%
20 1962
 
6.1%
16 1572
 
4.9%
11 1534
 
4.7%
12 1498
 
4.6%
5 1494
 
4.6%
8 1439
 
4.5%
2 1277
 
4.0%
7 1212
 
3.7%
Other values (92) 15840
49.0%
ValueCountFrequency (%)
2 1277
4.0%
3 874
 
2.7%
4 1150
3.6%
5 1494
4.6%
6 1114
3.4%
7 1212
3.7%
8 1439
4.5%
9 1060
3.3%
10 2507
7.8%
11 1534
4.7%
ValueCountFrequency (%)
105 24
0.1%
104 5
 
< 0.1%
103 4
 
< 0.1%
102 8
 
< 0.1%
100 15
< 0.1%
99 1
 
< 0.1%
98 2
 
< 0.1%
97 2
 
< 0.1%
96 4
 
< 0.1%
95 7
 
< 0.1%

product_width_cm
Real number (ℝ)

High correlation 

Distinct95
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.208464
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:03.426751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile47
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.080665
Coefficient of variation (CV)0.52052843
Kurtosis4.1173328
Mean23.208464
Median Absolute Deviation (MAD)6
Skewness1.6779106
Sum750260
Variance145.94247
MonotonicityNot monotonic
2025-09-13T10:46:03.739228image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3590
 
11.1%
20 2983
 
9.2%
16 2773
 
8.6%
15 2345
 
7.3%
30 1746
 
5.4%
12 1515
 
4.7%
25 1310
 
4.1%
14 1245
 
3.9%
13 1117
 
3.5%
17 1109
 
3.4%
Other values (85) 12594
39.0%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 4
 
< 0.1%
8 9
 
< 0.1%
9 15
 
< 0.1%
10 23
 
0.1%
11 3590
11.1%
12 1515
4.7%
13 1117
 
3.5%
14 1245
 
3.9%
15 2345
7.3%
ValueCountFrequency (%)
118 1
 
< 0.1%
105 5
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 13
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 1
 
< 0.1%
Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size252.7 KiB
2025-09-13T10:46:04.192585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length39
Median length31
Mean length12.940731
Min length3

Characters and Unicode

Total characters418335
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowperfumery
2nd rowart
3rd rowsports_leisure
4th rowbaby
5th rowhousewares
ValueCountFrequency (%)
bed_bath_table 3029
 
9.4%
sports_leisure 2867
 
8.9%
furniture_decor 2657
 
8.2%
health_beauty 2444
 
7.6%
housewares 2335
 
7.2%
auto 1900
 
5.9%
computers_accessories 1639
 
5.1%
toys 1411
 
4.4%
watches_gifts 1329
 
4.1%
telephony 1134
 
3.5%
Other values (61) 11582
35.8%
2025-09-13T10:46:04.834477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 49682
11.9%
s 40328
 
9.6%
t 36248
 
8.7%
o 31963
 
7.6%
r 29580
 
7.1%
a 28700
 
6.9%
_ 28163
 
6.7%
u 22359
 
5.3%
c 18863
 
4.5%
i 18640
 
4.5%
Other values (15) 113809
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 390077
93.2%
Connector Punctuation 28163
 
6.7%
Decimal Number 95
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 49682
12.7%
s 40328
10.3%
t 36248
 
9.3%
o 31963
 
8.2%
r 29580
 
7.6%
a 28700
 
7.4%
u 22359
 
5.7%
c 18863
 
4.8%
i 18640
 
4.8%
h 16390
 
4.2%
Other values (13) 97324
24.9%
Connector Punctuation
ValueCountFrequency (%)
_ 28163
100.0%
Decimal Number
ValueCountFrequency (%)
2 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 390077
93.2%
Common 28258
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 49682
12.7%
s 40328
10.3%
t 36248
 
9.3%
o 31963
 
8.2%
r 29580
 
7.6%
a 28700
 
7.4%
u 22359
 
5.7%
c 18863
 
4.8%
i 18640
 
4.8%
h 16390
 
4.2%
Other values (13) 97324
24.9%
Common
ValueCountFrequency (%)
_ 28163
99.7%
2 95
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 49682
11.9%
s 40328
 
9.6%
t 36248
 
8.7%
o 31963
 
7.6%
r 29580
 
7.1%
a 28700
 
6.9%
_ 28163
 
6.7%
u 22359
 
5.3%
c 18863
 
4.5%
i 18640
 
4.5%
Other values (15) 113809
27.2%

Interactions

2025-09-13T10:45:56.431955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:49.178856image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:50.352917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:51.741266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:52.915832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:54.116998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:55.225596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:56.589758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:49.382528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:50.743458image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:51.913139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:53.070353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:54.273883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:55.397464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:56.777243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:49.554395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:50.933062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:52.086231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:53.242773image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:54.468565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:55.553701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:56.959112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:49.741897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:51.102044image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:52.258660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:53.445891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:54.619543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:55.725564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:57.130963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:49.913739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:51.272550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:52.457440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:53.617748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:54.775762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:55.897425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:57.303409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:50.055460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:51.428790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:52.603355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:53.773985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:54.938364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:56.069668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:57.444030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:50.196074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:51.585030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:52.759593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:53.945187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:55.084418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-09-13T10:45:56.257740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-09-13T10:46:05.004932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
product_description_lenghtproduct_height_cmproduct_length_cmproduct_name_lenghtproduct_photos_qtyproduct_weight_gproduct_width_cm
product_description_lenght1.0000.100-0.0080.1000.1250.108-0.056
product_height_cm0.1001.0000.245-0.040-0.0390.5210.361
product_length_cm-0.0080.2451.0000.0800.0400.6150.612
product_name_lenght0.100-0.0400.0801.0000.1530.0990.068
product_photos_qty0.125-0.0390.0400.1531.0000.0100.002
product_weight_g0.1080.5210.6150.0990.0101.0000.603
product_width_cm-0.0560.3610.6120.0680.0020.6031.000

Missing values

2025-09-13T10:45:57.678369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-13T10:45:58.006615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_english
01e9e8ef04dbcff4541ed26657ea517e5perfumaria40.0287.01.0225.016.010.014.0perfumery
13aa071139cb16b67ca9e5dea641aaa2fartes44.0276.01.01000.030.018.020.0art
296bd76ec8810374ed1b65e291975717fesporte_lazer46.0250.01.0154.018.09.015.0sports_leisure
3cef67bcfe19066a932b7673e239eb23dbebes27.0261.01.0371.026.04.026.0baby
49dc1a7de274444849c219cff195d0b71utilidades_domesticas37.0402.04.0625.020.017.013.0housewares
541d3672d4792049fa1779bb35283ed13instrumentos_musicais60.0745.01.0200.038.05.011.0musical_instruments
6732bd381ad09e530fe0a5f457d81becbcool_stuff56.01272.04.018350.070.024.044.0cool_stuff
72548af3e6e77a690cf3eb6368e9ab61emoveis_decoracao56.0184.02.0900.040.08.040.0furniture_decor
837cc742be07708b53a98702e77a21a02eletrodomesticos57.0163.01.0400.027.013.017.0home_appliances
98c92109888e8cdf9d66dc7e463025574brinquedos36.01156.01.0600.017.010.012.0toys
product_idproduct_category_nameproduct_name_lenghtproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_english
323176ec96c91757fad0aecafc0ee7f262dccbebes62.01417.01.09550.036.035.035.0baby
3231816280ca280a86fee2ba3c928ed04439fmoveis_decoracao64.0236.011.02200.031.011.026.0furniture_decor
323193becff10d1deb92b02f2a1ee62a04524informatica_acessorios54.01520.02.06150.030.030.020.0computers_accessories
323201a14237ecc2fe3772b55c8d4e11ccb35moveis_decoracao58.01405.03.0150.035.02.025.0furniture_decor
32321c4e71b64511b959455e2107fe7859020utilidades_domesticas59.01371.02.0200.018.015.015.0housewares
32322a0b7d5a992ccda646f2d34e418fff5a0moveis_decoracao45.067.02.012300.040.040.040.0furniture_decor
32323bf4538d88321d0fd4412a93c974510e6construcao_ferramentas_iluminacao41.0971.01.01700.016.019.016.0construction_tools_lights
323249a7c6041fa9592d9d9ef6cfe62a71f8ccama_mesa_banho50.0799.01.01400.027.07.027.0bed_bath_table
3232583808703fc0706a22e264b9d75f04a2einformatica_acessorios60.0156.02.0700.031.013.020.0computers_accessories
32326106392145fca363410d287a815be6de4cama_mesa_banho58.0309.01.02083.012.02.07.0bed_bath_table